ABSTRACT Over the past 30 years, the US prevalence of adult obesity has more than doubled, resulting in ~96 million obese Americans in 2016. US minority populations shoulder the majority of the obesity burden, with African Americans (AA) having the highest age-adjusted prevalence of overall and central obesity. These disparities are accompanied by rises in obesity-related morbidity, mortality, and health care expenditures, most notably from cardiovascular diseases (CVD). However, not all obese individuals have the same risk for adverse health outcomes. Central obesity is more metabolically active and contributes disproportionately to poor health compared to overall obesity. This implies that the distribution of body fat may have distinct health consequences, and distinct underpinnings, including genetic predisposition. And, while genome-wide association studies (GWAS) have identified over 500 genomic regions associated with obesity phenotypes, few genes have been functionally validated, making it difficult to move GWAS findings into the clinic to improve patient health. Precisely measuring obesity and fat distribution may help move the field of obesity genomics forward. As obesity can result from dysregulation of energy balance, metabolites are a logical means to refine phenotypic definitions of obesity, and narrow in on the most likely causal genes underlying GWAS signals. The proposed study will leverage existing genetic, phenotypic, and metabolomic data from both European American and African American participants in the Atherosclerosis Risk in Communities (ARIC) Study in order to: 1) evaluate the association between metabolomic profiles and overall obesity and central obesity in ARIC to identify unique metabolomic profiles (metabotypes) that differ by body mass and body fat distribution patterns; and 2) examine genetic effects on obesity-associated metabolites (mGWAS) in ARIC to identify unique genetic underpinnings of obesity-associated metabolites and metabolic profiles, with replication and validation of both aims in the Multi-Ethnic Study of Atherosclerosis (MESA). Systematically evaluating the influences of obesity distribution and metabolomic profiles could provide important, but largely unexplored, insights into the pathogenesis of obesity and inform prevention strategies and treatment guidelines. Integrating metabolomics into GWAS will also provide the opportunity to identify the best candidate genes around GWAS signals for functional follow-up in future laboratory experiments, giving potentially actionable biological relevance to the hundreds of genetic signals for obesity.